The state of the art commercial query optimizers employ cost-based optimization and exploit dynamic programming (DP) to find the optimal query execution plan (QEP) without evaluating redundant sub-plans. The number of alternative QEPs enumerated by the DP query optimizer can increase exponentially, as the number of joins in the query increases. Recently, by exploiting the coming wave of multi-core processor architectures, a state of the art parallel optimization algorithm [14], referred to as PDPsva, has been proposed to parallelize the “time-consuming” DP query optimization process itself. While PDPsva significantly extends the practical use of DP to queries having up to 20-25 tables, it has several limitations: 1) supporting only the size-driven DP enumerator, 2) statically allocating search space, and 3) not fully exploiting parallelism. In this paper, we propose the first generic solution for parallelizing any type of bottom-up optimizer, including the graph-traversal drive...